Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients

Abstract

Background: Multiple sclerosis (MS) is the most common non-traumatic disabling disease.Objective: The aim of this study is to investigate the ability of radiomics features for diagnosing active plaques in patients with MS from T2 Fluid Attenuated Inversion Recovery (FLAIR) images.Material and Methods: In this experimental study, images of 82 patients with 122 MS lesions were investigated. Boruta and Relief algorithms were used for feature selection on the train data set (70%). Four different classifier algorithms, including Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Decision Tree (DT), and Extreme Gradient Boosting (XGB) were used as classifiers for modeling. Finally, Performance metrics were obtained on the test data set (30%) with 1000 bootstrap and 95% confidence intervals (95% CIs).Results: A total of 107 radiomics features were extracted for each lesion, of which 7 and 8 features were selected by the Relief method and Boruta method, respectively. DT classifier had the best performance in the two feature selection algorithms. The best performance on the test data set was related to Boruta-DT with an average accuracy of 0.86, sensitivity of 1.00, specificity of 0.84, and Area Under the Curve (AUC) of 0.92 (95% CI: 0.92-0.92). Conclusion: Radiomics features have the potential for diagnosing MS active plaque by T2 FLAIR image features. Additionally, choosing the feature selection and classifier algorithms plays an important role in the diagnosis of active plaque in MS patients. The radiomics-based predictive models predict active lesions accurately and non-invasively.

Keywords

• multiple sclerosis
• machine learning
• magnetic resonance imaging
• radiomics
• flair